hGA: Hybrid Genetic Algorithm in Fuzzy Rule-based Classification Systems for High-dimensional Problems

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Küçük Resim

Tarih

2012

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Elsevier Science Bv

Erişim Hakkı

info:eu-repo/semantics/openAccess

Özet

The aim of this work is to propose a hybrid heuristic approach (called hGA) based on genetic algorithm (GA) and integer-programming formulation (IPF) to solve high dimensional classification problems in linguistic fuzzy rule-based classification systems. In this algorithm, each chromosome represents a rule for specified class, GA is used for producing several rules for each class, and finally IPF is used for selection of rules from a pool of rules, which are obtained by GA. The proposed algorithm is experimentally evaluated by the use of non-parametric statistical tests on seventeen classification benchmark data sets. Results of the comparative study show that hGA is able to discover accurate and concise classification rules. Published by Elsevier B.V.

Açıklama

Anahtar Kelimeler

Fuzzy rule based classification systems, Genetic algorithms, Genetic fuzzy systems, Classification, Integer programming

Kaynak

Applied Soft Computing

WoS Q Değeri

Q1

Scopus Q Değeri

Q1

Cilt

12

Sayı

2

Künye

Kızılkaya Aydogan, E., Karaoglan, I., Pardalos, P. M., (2012). hGA: Hybrid Genetic Algorithm in Fuzzy Rule-based Classification Systems for High-dimensional Problems. Applied Soft Computing. 12(2), 800-806. doi:10.1016/j.asoc.2011.10.010